Seung Il Lee

I'm Seung Il Lee. My research focuses on multimodal language models and AI agents. I am interested in leveraging multimodal models to manage and control intelligent agents.

Email  /  Github  /  Blog  /  Tistory

Publications

We propose a token-based affordance grounding framework using Large Vision-Language Models (LVLMs) to localize action-supporting regions in an image.

Authored a technical book covering Web 3.0 implementation, blockchain network architecture, and hands-on Python development.

Professional Experience

• Global AI Product Launch (NYSE): Architected the AI-driven investment logic and container-based infrastructure (AWS) for the GXIG ETF / news, successfully listed on the NYSE.
• Cloud-Native Collaboration: Established a global collaborative development environment using Git and AWS EKS, co-working with New York-based teams to ensure system consistency.
• AI Asset Management: Designed and built an AI-based wealth management system, automating portfolio rebalancing and risk assessment using Python and Spark.
• Internal Process Automation: Developed an LLM-based automation tool for internal employee and client management on GCP and AWS, reducing manual operational tasks.

• Enterprise LLM Agent: Built and deployed an LLM-based financial data agent serving 3 million retail customers / news.
• Real-time AI Pipeline: Engineered a high-performance data pipeline using LangChain and Kafka to ingest and process unstructured data from Reuters, CNBC, and SEC EDGAR.
• Large-scale Data Analysis: Utilized PySpark to analyze behavioral data of 3 million customers, providing personalized investment insights through AI models.
• Scalable RAG Architecture: Developed a production-grade RAG pipeline to ensure high-accuracy information retrieval from real-time global financial news feeds.

• Real-time IoT Pipeline: Built a Kafka-based streaming data pipeline on AWS to process TB-scale real-time vehicle sensor data.
• Driving Risk Analytics: Developed algorithms to analyze driving patterns and measure safety risks, providing foundational data for insurance scoring models.
• Serverless Architecture: Optimized cloud costs and performance by implementing serverless components (Lambda, Athena) for geospatial data analysis.

• Operational Performance (OEE): Researched and designed the OEE analytical framework from first principles to optimize semiconductor manufacturing efficiency.
• Predictive Risk Modeling: Developed statistical "Down-risk" models to identify equipment failure patterns, reducing unplanned downtime in the production line.
• Data-driven Decision Support: Translated complex manufacturing logs into structured KPIs for executive-level strategic production planning.

• Data-driven Strategy: Established mid-to-long term business strategy for Kia Motors based on data analysis.

Honors & Awards

• First Prize in U+ Customer Idea Contest | LG Uplus | 2022
• First Prize in MyData Contest | Ministry of the Interior and Safety (MOIS) | 2022
• Grand Prize for Blockchain Challenge | Korea Internet & Security Agency (KISA) | 2022
• Excellence Award in News Big Data Hackathon | Korea Press Foundation | 2022
• Award for Production Optimization through Equipment Log Analysis | Samsung Electronics | 2018

Feel free to steal this website's source code.